On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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9 results for “tensor-kernels”

Generalized support vector regression: duality and tensor-kernel representationmath.OC

Paper solves support vector regression in Banach spaces using tensor-kernels.

problem Support vector regression in Banach function spaces.

method Fenchel-Rockafellar duality theory and tensor-kernel representation.

result Explicit formulation of dual problem and optimality conditions.

support vector regression Banach spaces tensor-kernels Fenchel-Rockafellar duality optimality conditions

2016-03-18abs ↗pdf ↗

Solving

\ell^p\!

-norm regularization with tensor kernelsstat.ML

Paper proposes efficient tensor kernels for solving nonparametric $\ell^p$ regularization.

problem Nonparametric extensions of

\ell^p

regularization.

method Fast dual algorithm using tensor kernels.

result Efficient solution to nonparametric

\ell^p

regularization problems.

tensor kernels nonparametric ell p regularization dual algorithm

2017-07-18abs ↗pdf ↗

Tensor Kernel Recovery for Spatio-Temporal Hawkes Processesstat.ML

Estimates spatio-temporal Hawkes processes using tensor recovery.

problem Estimating influence functions for spatio-temporal Hawkes processes.

method Formulates influence function as a tensor kernel, assumes low-rank structure, solves as convex optimization problem.

result Provides theoretical guarantees and demonstrates efficiency with simulations.

Tensor Kernel Hawkes Processes Spatio-Temporal Influence Functions Convex Optimization

2020-11-24abs ↗pdf ↗

Efficient Tensor Kernel methods for sparse regressioncs.LG

Efficient tensor kernel method reduces memory usage and computational cost for sparse regression.

problem Memory and computational limitations in tensor kernel methods for sparse regression.

method Proposes a new tensor data layout and Nystrom subsampling approach to reduce memory and computational requirements.

result Improvements lead to more efficient tensor kernel methods for sparse regression.

sparse regression tensor kernels memory reduction Nystrom subsampling lp-norm regularization

2020-03-23abs ↗pdf ↗

Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machineeess.SP

Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.

problem Improving seizure detection accuracy for wearable devices.

method Transfer learning with tensor kernel machine using canonical polyadic decomposition.

result Patient fine-tuned model achieves high performance with smaller model size.

seizure detection wearable devices transfer learning tensor kernel machine canonical polyadic decomposition

2024-08-01abs ↗pdf ↗

Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernelsstat.ML

New IP analysis for deep neural networks using Rényi's entropy and tensor kernels.

problem Estimating mutual information in high-dimensional hidden layers of deep neural networks.

method Matrix-based Rényi's entropy coupled with tensor kernels for convolutional layers.

result First comprehensive IP analysis of large-scale DNNs and CNNs.

information plane deep neural networks Rényi's entropy tensor kernels convolutional layers

2019-09-25abs ↗pdf ↗

Leveraging tensor kernels to reduce objective function mismatch in deep clusteringstat.ML

New tensor kernels reduce mismatch between clustering and reconstruction objectives in deep learning.

problem Objective Function Mismatch in deep clustering.

method Proposed Unsupervised Companion Objectives (UCOs) with tensor kernels to address mismatch.

result Reduced OFM between clustering and reconstruction objectives, leading to improved clustering performance.

Deep clustering Objective Function Mismatch Tensor kernels Unsupervised Companion Objectives Reconstruction objectives

2020-01-20abs ↗pdf ↗

Stochastic Gradients for Large-Scale Tensor Decompositionmath.NA

Efficiently decomposes large tensors using stochastic gradients.

problem Efficiently decomposing large tensors for multiway data analysis.

method Stochastic gradients computed via MTTKRP kernel for efficient computation.

result Advantages and scalability demonstrated for large-scale problems.

tensor decomposition stochastic gradients large-scale tensors GCP decomposition MTTKRP kernel

2019-06-04abs ↗pdf ↗

Generalized Canonical Polyadic Tensor Decompositions with General Symmetrymath.NA

Develops SymGCP for tensor decompositions with general symmetry.

problem Handling symmetry in tensor decompositions for better model accuracy.

method Introduces SymGCP, a generalized CP decomposition that accounts for any subset of tensor modes' symmetry.

result SymGCP enables efficient and scalable tensor decomposition with improved model robustness and accuracy.

tensor decomposition symmetry generalized CP graph adjacency matrices stochastic optimization

2026-01-08abs ↗pdf ↗